Identification of hospital catchment areas using clustering: an example from the NHS

Abstract

Objective: To develop a method of hospital market area identification using multivariate data, and compare it with existing standard methods.

Data sources: Hospital Episode Statistics, a secondary dataset of admissions data from all hospitals in England, between April 2005 and March 2006.

Study design: Seven criteria for catchment area definition were proposed. K-means clustering was used on several variables describing the relationship between hospitals and local authority districts (LADs) to enable the placement of every LAD into or out of the catchment area for every hospital. Principal component analysis confirmed the statistical robustness of the method, and the method was compared against existing methods using the seven criteria.

Principal findings: Existing methods for identifying catchment areas do not capture desirable properties of a hospital market area. Catchment areas identified using K-means clustering are superior to those identified using existing Marginal methods against these criteria and are also statistically robust.

Conclusions: K-means clustering uses multivariate data on the relationship between hospitals and geographical units to define catchment areas that are both statistically robust and more informative than those obtained from existing methods.

Figure 1

Catchment Area Size and Minimum Percentage for Inclusion in Catchment Area by Total Hospital Admissions Note. Plots of number of LADs included in catchment area (a and b) and minimum threshold percentage for inclusion in the catchment area (c and d) for the K-means and the Marginal methods. Fitted lines from the power law (in the log-log transformed data) are plotted on every graph, and the R² value for each model is shown on the chart.

Figure 2

Proportion of Total Visits and Variance Included in the Catchment Area by Model Type Note. Tests of relative proportions of all hospital admissions or variance explained by the two methods. (a) plots the proportion of the total hospital admissions that comes from the catchment area for the two methods. The line in this figure indicates the points, where the K-means and Marginal methods capture the same proportion of total hospital admissions. For all hospitals to the left of this line, the K-means method captures a higher proportion of admissions than the Marginal; for hospitals to the right of the line, the opposite applies. (b) shows the proportion of total variance that is explained by PC1. (c) and (d) plot the two axes of (a) separately, against total hospital admissions, with y-axes on the same scale. Neither plot is well explained by a power law.

Figure 3

Proportion of LADs Assigned into Market Areas by K-Means and Marginal Methods

Figure 4

University College London Hospital, K-Means and Marginal Methods